Hierarchical CXR-Net: A Two-Stage Interpretable Framework for Efficient and Interpretable Chest X-Ray Diagnosis

The increasing volume of daily chest X-ray examinations places a significant burden on clinical workflows, as most scans are normal but still require expert review, delaying the diagnosis of critical conditions. Many of existing deep learning models are either computationally heavy and unsuitable for triage or lack transparency. This study aimed to develop an efficient, interpretable, and reproducible hierarchical model aligned with real clinical practice. We proposed Hierarchical chest X-ray Network, a two-stage framework built entirely on public dataset. Stege 1 utilised a lightweight EfficientNet-B0 model, selected through rigorous competitive experiment, to rapidly triage and prioritise potentially abnormal cases. Stage 2 employed a more powerful EfficientNet-B2 model, also empirically validated, to perform 14-class multi-label classification on the prioritised images. The Stage 1 screener achieved a test area under the receiver operating characteristics curve of 0.831, demonstrating efficient and imbalance-robust screening performance. The Stage 2 expert model achieved a mean area under the receiver operating characteristics curve of 0.814 across 14 pathologies, providing strong diagnostic capabilities. Hierarchical chest X-ray Network enhances workflow efficiency while improving transparency and reproducibility compared to traditional single-stage models. Its two-step, workflow-oriented architecture offers a practical, interpretable solution suitable for integration into real-world clinical settings.